Overview

Dataset statistics

Number of variables29
Number of observations4410
Missing cells111
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory999.3 KiB
Average record size in memory232.0 B

Variable types

Numeric11
Boolean2
Categorical16

Alerts

EmployeeCount has constant value ""Constant
Over18 has constant value ""Constant
StandardHours has constant value ""Constant
Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with EducationFieldHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly overall correlated with Age and 1 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompanyHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompanyHigh correlation
EmployeeID is uniformly distributedUniform
EmployeeID has unique valuesUnique
NumCompaniesWorked has 586 (13.3%) zerosZeros
TrainingTimesLastYear has 162 (3.7%) zerosZeros
YearsAtCompany has 132 (3.0%) zerosZeros
YearsSinceLastPromotion has 1743 (39.5%) zerosZeros
YearsWithCurrManager has 789 (17.9%) zerosZeros

Reproduction

Analysis started2024-01-25 14:28:27.366341
Analysis finished2024-01-25 14:28:44.863135
Duration17.5 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

EmployeeID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4410
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2205.5
Minimum1
Maximum4410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:45.129660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile221.45
Q11103.25
median2205.5
Q33307.75
95-th percentile4189.55
Maximum4410
Range4409
Interquartile range (IQR)2204.5

Descriptive statistics

Standard deviation1273.2017
Coefficient of variation (CV)0.57728482
Kurtosis-1.2
Mean2205.5
Median Absolute Deviation (MAD)1102.5
Skewness0
Sum9726255
Variance1621042.5
MonotonicityStrictly increasing
2024-01-25T19:58:45.246168image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2938 1
 
< 0.1%
2944 1
 
< 0.1%
2943 1
 
< 0.1%
2942 1
 
< 0.1%
2941 1
 
< 0.1%
2940 1
 
< 0.1%
2939 1
 
< 0.1%
2937 1
 
< 0.1%
2963 1
 
< 0.1%
Other values (4400) 4400
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4410 1
< 0.1%
4409 1
< 0.1%
4408 1
< 0.1%
4407 1
< 0.1%
4406 1
< 0.1%
4405 1
< 0.1%
4404 1
< 0.1%
4403 1
< 0.1%
4402 1
< 0.1%
4401 1
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:45.361676image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1333013
Coefficient of variation (CV)0.24735533
Kurtosis-0.40595054
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.41300495
Sum162834
Variance83.417192
MonotonicityNot monotonic
2024-01-25T19:58:45.471499image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 234
 
5.3%
34 231
 
5.2%
31 207
 
4.7%
36 207
 
4.7%
29 204
 
4.6%
32 183
 
4.1%
30 180
 
4.1%
38 174
 
3.9%
33 174
 
3.9%
40 171
 
3.9%
Other values (33) 2445
55.4%
ValueCountFrequency (%)
18 24
 
0.5%
19 27
 
0.6%
20 33
 
0.7%
21 39
 
0.9%
22 48
 
1.1%
23 42
 
1.0%
24 78
1.8%
25 78
1.8%
26 117
2.7%
27 144
3.3%
ValueCountFrequency (%)
60 15
 
0.3%
59 30
0.7%
58 42
1.0%
57 12
 
0.3%
56 42
1.0%
55 66
1.5%
54 54
1.2%
53 57
1.3%
52 54
1.2%
51 57
1.3%

Attrition
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
3699 
True
711 
ValueCountFrequency (%)
False 3699
83.9%
True 711
 
16.1%
2024-01-25T19:58:45.593521image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Travel_Rarely
3129 
Travel_Frequently
831 
Non-Travel
450 

Length

Max length17
Median length13
Mean length13.447619
Min length10

Characters and Unicode

Total characters59304
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Frequently
4th rowNon-Travel
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 3129
71.0%
Travel_Frequently 831
 
18.8%
Non-Travel 450
 
10.2%

Length

2024-01-25T19:58:45.686029image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:45.808527image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 3129
71.0%
travel_frequently 831
 
18.8%
non-travel 450
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 9201
15.5%
r 8370
14.1%
l 8370
14.1%
a 7539
12.7%
T 4410
7.4%
v 4410
7.4%
y 3960
6.7%
_ 3960
6.7%
R 3129
 
5.3%
n 1281
 
2.2%
Other values (7) 4674
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46074
77.7%
Uppercase Letter 8820
 
14.9%
Connector Punctuation 3960
 
6.7%
Dash Punctuation 450
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9201
20.0%
r 8370
18.2%
l 8370
18.2%
a 7539
16.4%
v 4410
9.6%
y 3960
8.6%
n 1281
 
2.8%
q 831
 
1.8%
u 831
 
1.8%
t 831
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 4410
50.0%
R 3129
35.5%
F 831
 
9.4%
N 450
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_ 3960
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54894
92.6%
Common 4410
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9201
16.8%
r 8370
15.2%
l 8370
15.2%
a 7539
13.7%
T 4410
8.0%
v 4410
8.0%
y 3960
7.2%
R 3129
 
5.7%
n 1281
 
2.3%
F 831
 
1.5%
Other values (5) 3393
 
6.2%
Common
ValueCountFrequency (%)
_ 3960
89.8%
- 450
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9201
15.5%
r 8370
14.1%
l 8370
14.1%
a 7539
12.7%
T 4410
7.4%
v 4410
7.4%
y 3960
6.7%
_ 3960
6.7%
R 3129
 
5.3%
n 1281
 
2.2%
Other values (7) 4674
7.9%

Department
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Research & Development
2883 
Sales
1338 
Human Resources
 
189

Length

Max length22
Median length22
Mean length16.542177
Min length5

Characters and Unicode

Total characters72951
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 2883
65.4%
Sales 1338
30.3%
Human Resources 189
 
4.3%

Length

2024-01-25T19:58:45.910043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:46.025558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
research 2883
27.8%
2883
27.8%
development 2883
27.8%
sales 1338
12.9%
human 189
 
1.8%
resources 189
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 16131
22.1%
5955
 
8.2%
s 4599
 
6.3%
a 4410
 
6.0%
l 4221
 
5.8%
R 3072
 
4.2%
r 3072
 
4.2%
c 3072
 
4.2%
n 3072
 
4.2%
m 3072
 
4.2%
Other values (10) 22275
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56631
77.6%
Uppercase Letter 7482
 
10.3%
Space Separator 5955
 
8.2%
Other Punctuation 2883
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16131
28.5%
s 4599
 
8.1%
a 4410
 
7.8%
l 4221
 
7.5%
r 3072
 
5.4%
c 3072
 
5.4%
n 3072
 
5.4%
m 3072
 
5.4%
o 3072
 
5.4%
p 2883
 
5.1%
Other values (4) 9027
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 3072
41.1%
D 2883
38.5%
S 1338
17.9%
H 189
 
2.5%
Space Separator
ValueCountFrequency (%)
5955
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64113
87.9%
Common 8838
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16131
25.2%
s 4599
 
7.2%
a 4410
 
6.9%
l 4221
 
6.6%
R 3072
 
4.8%
r 3072
 
4.8%
c 3072
 
4.8%
n 3072
 
4.8%
m 3072
 
4.8%
o 3072
 
4.8%
Other values (8) 16320
25.5%
Common
ValueCountFrequency (%)
5955
67.4%
& 2883
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16131
22.1%
5955
 
8.2%
s 4599
 
6.3%
a 4410
 
6.0%
l 4221
 
5.8%
R 3072
 
4.2%
r 3072
 
4.2%
c 3072
 
4.2%
n 3072
 
4.2%
m 3072
 
4.2%
Other values (10) 22275
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:46.125578image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1050255
Coefficient of variation (CV)0.88169818
Kurtosis-0.22704535
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.95746575
Sum40539
Variance65.691439
MonotonicityNot monotonic
2024-01-25T19:58:46.223094image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 633
14.4%
1 624
14.1%
10 258
 
5.9%
9 255
 
5.8%
3 252
 
5.7%
7 252
 
5.7%
8 240
 
5.4%
5 195
 
4.4%
4 192
 
4.4%
6 177
 
4.0%
Other values (19) 1332
30.2%
ValueCountFrequency (%)
1 624
14.1%
2 633
14.4%
3 252
 
5.7%
4 192
 
4.4%
5 195
 
4.4%
6 177
 
4.0%
7 252
 
5.7%
8 240
 
5.4%
9 255
5.8%
10 258
5.9%
ValueCountFrequency (%)
29 81
1.8%
28 69
1.6%
27 36
0.8%
26 75
1.7%
25 75
1.7%
24 84
1.9%
23 81
1.8%
22 57
1.3%
21 54
1.2%
20 75
1.7%

Education
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
1716 
4
1194 
2
846 
1
510 
5
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row4
4th row5
5th row1

Common Values

ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

Length

2024-01-25T19:58:46.326419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:46.434546image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1716
38.9%
4 1194
27.1%
2 846
19.2%
1 510
 
11.6%
5 144
 
3.3%

EducationField
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Life Sciences
1818 
Medical
1392 
Marketing
477 
Technical Degree
396 
Other
246 

Length

Max length16
Median length15
Mean length10.533333
Min length5

Characters and Unicode

Total characters46452
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 1818
41.2%
Medical 1392
31.6%
Marketing 477
 
10.8%
Technical Degree 396
 
9.0%
Other 246
 
5.6%
Human Resources 81
 
1.8%

Length

2024-01-25T19:58:46.540571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:46.660719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
life 1818
27.1%
sciences 1818
27.1%
medical 1392
20.8%
marketing 477
 
7.1%
technical 396
 
5.9%
degree 396
 
5.9%
other 246
 
3.7%
human 81
 
1.2%
resources 81
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 9315
20.1%
i 5901
12.7%
c 5901
12.7%
n 2772
 
6.0%
a 2346
 
5.1%
2295
 
4.9%
s 1980
 
4.3%
M 1869
 
4.0%
L 1818
 
3.9%
f 1818
 
3.9%
Other values (16) 10437
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37452
80.6%
Uppercase Letter 6705
 
14.4%
Space Separator 2295
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9315
24.9%
i 5901
15.8%
c 5901
15.8%
n 2772
 
7.4%
a 2346
 
6.3%
s 1980
 
5.3%
f 1818
 
4.9%
l 1788
 
4.8%
d 1392
 
3.7%
r 1200
 
3.2%
Other values (7) 3039
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M 1869
27.9%
L 1818
27.1%
S 1818
27.1%
T 396
 
5.9%
D 396
 
5.9%
O 246
 
3.7%
H 81
 
1.2%
R 81
 
1.2%
Space Separator
ValueCountFrequency (%)
2295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44157
95.1%
Common 2295
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9315
21.1%
i 5901
13.4%
c 5901
13.4%
n 2772
 
6.3%
a 2346
 
5.3%
s 1980
 
4.5%
M 1869
 
4.2%
L 1818
 
4.1%
f 1818
 
4.1%
S 1818
 
4.1%
Other values (15) 8619
19.5%
Common
ValueCountFrequency (%)
2295
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9315
20.1%
i 5901
12.7%
c 5901
12.7%
n 2772
 
6.0%
a 2346
 
5.1%
2295
 
4.9%
s 1980
 
4.3%
M 1869
 
4.0%
L 1818
 
3.9%
f 1818
 
3.9%
Other values (16) 10437
22.5%

EmployeeCount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
4410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4410
100.0%

Length

2024-01-25T19:58:46.765722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:46.859596image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4410
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4410
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4410
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4410
100.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Male
2646 
Female
1764 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters21168
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 2646
60.0%
Female 1764
40.0%

Length

2024-01-25T19:58:46.945576image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:47.055552image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
male 2646
60.0%
female 1764
40.0%

Most occurring characters

ValueCountFrequency (%)
e 6174
29.2%
a 4410
20.8%
l 4410
20.8%
M 2646
12.5%
F 1764
 
8.3%
m 1764
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16758
79.2%
Uppercase Letter 4410
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6174
36.8%
a 4410
26.3%
l 4410
26.3%
m 1764
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M 2646
60.0%
F 1764
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21168
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6174
29.2%
a 4410
20.8%
l 4410
20.8%
M 2646
12.5%
F 1764
 
8.3%
m 1764
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6174
29.2%
a 4410
20.8%
l 4410
20.8%
M 2646
12.5%
F 1764
 
8.3%
m 1764
 
8.3%

JobLevel
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
1629 
2
1602 
3
654 
4
318 
5
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row4
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

Length

2024-01-25T19:58:47.138494image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:47.243534image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1629
36.9%
2 1602
36.3%
3 654
14.8%
4 318
 
7.2%
5 207
 
4.7%

JobRole
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Sales Executive
978 
Research Scientist
876 
Laboratory Technician
777 
Manufacturing Director
435 
Healthcare Representative
393 
Other values (4)
951 

Length

Max length25
Median length21
Mean length18.070748
Min length7

Characters and Unicode

Total characters79692
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealthcare Representative
2nd rowResearch Scientist
3rd rowSales Executive
4th rowHuman Resources
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive 978
22.2%
Research Scientist 876
19.9%
Laboratory Technician 777
17.6%
Manufacturing Director 435
9.9%
Healthcare Representative 393
8.9%
Manager 306
 
6.9%
Sales Representative 249
 
5.6%
Research Director 240
 
5.4%
Human Resources 156
 
3.5%

Length

2024-01-25T19:58:47.349556image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:47.485911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
sales 1227
14.4%
research 1116
13.1%
executive 978
11.5%
scientist 876
10.3%
laboratory 777
9.1%
technician 777
9.1%
director 675
7.9%
representative 642
7.5%
manufacturing 435
 
5.1%
healthcare 393
 
4.6%
Other values (3) 618
7.3%

Most occurring characters

ValueCountFrequency (%)
e 11715
14.7%
a 7740
 
9.7%
t 6294
 
7.9%
c 6183
 
7.8%
i 6036
 
7.6%
r 5952
 
7.5%
n 4404
 
5.5%
s 4173
 
5.2%
4104
 
5.1%
o 2385
 
3.0%
Other values (19) 20706
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67074
84.2%
Uppercase Letter 8514
 
10.7%
Space Separator 4104
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11715
17.5%
a 7740
11.5%
t 6294
9.4%
c 6183
9.2%
i 6036
9.0%
r 5952
8.9%
n 4404
 
6.6%
s 4173
 
6.2%
o 2385
 
3.6%
h 2286
 
3.4%
Other values (10) 9906
14.8%
Uppercase Letter
ValueCountFrequency (%)
S 2103
24.7%
R 1914
22.5%
E 978
11.5%
L 777
 
9.1%
T 777
 
9.1%
M 741
 
8.7%
D 675
 
7.9%
H 549
 
6.4%
Space Separator
ValueCountFrequency (%)
4104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75588
94.9%
Common 4104
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11715
15.5%
a 7740
10.2%
t 6294
 
8.3%
c 6183
 
8.2%
i 6036
 
8.0%
r 5952
 
7.9%
n 4404
 
5.8%
s 4173
 
5.5%
o 2385
 
3.2%
h 2286
 
3.0%
Other values (18) 18420
24.4%
Common
ValueCountFrequency (%)
4104
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11715
14.7%
a 7740
 
9.7%
t 6294
 
7.9%
c 6183
 
7.8%
i 6036
 
7.6%
r 5952
 
7.5%
n 4404
 
5.5%
s 4173
 
5.2%
4104
 
5.1%
o 2385
 
3.0%
Other values (19) 20706
26.0%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Married
2019 
Single
1410 
Divorced
981 

Length

Max length8
Median length7
Mean length6.9027211
Min length6

Characters and Unicode

Total characters30441
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 2019
45.8%
Single 1410
32.0%
Divorced 981
22.2%

Length

2024-01-25T19:58:47.617606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:47.733783image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
married 2019
45.8%
single 1410
32.0%
divorced 981
22.2%

Most occurring characters

ValueCountFrequency (%)
r 5019
16.5%
i 4410
14.5%
e 4410
14.5%
d 3000
9.9%
M 2019
6.6%
a 2019
6.6%
S 1410
 
4.6%
n 1410
 
4.6%
g 1410
 
4.6%
l 1410
 
4.6%
Other values (4) 3924
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26031
85.5%
Uppercase Letter 4410
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5019
19.3%
i 4410
16.9%
e 4410
16.9%
d 3000
11.5%
a 2019
7.8%
n 1410
 
5.4%
g 1410
 
5.4%
l 1410
 
5.4%
v 981
 
3.8%
o 981
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 2019
45.8%
S 1410
32.0%
D 981
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 30441
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5019
16.5%
i 4410
14.5%
e 4410
14.5%
d 3000
9.9%
M 2019
6.6%
a 2019
6.6%
S 1410
 
4.6%
n 1410
 
4.6%
g 1410
 
4.6%
l 1410
 
4.6%
Other values (4) 3924
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 5019
16.5%
i 4410
14.5%
e 4410
14.5%
d 3000
9.9%
M 2019
6.6%
a 2019
6.6%
S 1410
 
4.6%
n 1410
 
4.6%
g 1410
 
4.6%
l 1410
 
4.6%
Other values (4) 3924
12.9%

MonthlyIncome
Real number (ℝ)

Distinct1349
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65029.313
Minimum10090
Maximum199990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:47.841835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile20970
Q129110
median49190
Q383800
95-th percentile178560
Maximum199990
Range189900
Interquartile range (IQR)54690

Descriptive statistics

Standard deviation47068.889
Coefficient of variation (CV)0.72381033
Kurtosis1.0002319
Mean65029.313
Median Absolute Deviation (MAD)21990
Skewness1.3688842
Sum2.8677927 × 108
Variance2.2154803 × 109
MonotonicityNot monotonic
2024-01-25T19:58:47.960656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23420 12
 
0.3%
61420 9
 
0.2%
27410 9
 
0.2%
26100 9
 
0.2%
23800 9
 
0.2%
55620 9
 
0.2%
25590 9
 
0.2%
24040 9
 
0.2%
63470 9
 
0.2%
34520 9
 
0.2%
Other values (1339) 4317
97.9%
ValueCountFrequency (%)
10090 3
0.1%
10510 3
0.1%
10520 3
0.1%
10810 3
0.1%
10910 3
0.1%
11020 3
0.1%
11180 3
0.1%
11290 3
0.1%
12000 3
0.1%
12230 3
0.1%
ValueCountFrequency (%)
199990 3
0.1%
199730 3
0.1%
199430 3
0.1%
199260 3
0.1%
198590 3
0.1%
198470 3
0.1%
198450 3
0.1%
198330 3
0.1%
197400 3
0.1%
197170 3
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.2%
Missing19
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.6948303
Minimum0
Maximum9
Zeros586
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:48.066726image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4988869
Coefficient of variation (CV)0.92728913
Kurtosis0.0072874809
Mean2.6948303
Median Absolute Deviation (MAD)1
Skewness1.0267667
Sum11833
Variance6.2444357
MonotonicityNot monotonic
2024-01-25T19:58:48.144435image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1558
35.3%
0 586
 
13.3%
3 474
 
10.7%
2 438
 
9.9%
4 415
 
9.4%
7 222
 
5.0%
6 208
 
4.7%
5 187
 
4.2%
9 156
 
3.5%
8 147
 
3.3%
(Missing) 19
 
0.4%
ValueCountFrequency (%)
0 586
 
13.3%
1 1558
35.3%
2 438
 
9.9%
3 474
 
10.7%
4 415
 
9.4%
5 187
 
4.2%
6 208
 
4.7%
7 222
 
5.0%
8 147
 
3.3%
9 156
 
3.5%
ValueCountFrequency (%)
9 156
 
3.5%
8 147
 
3.3%
7 222
 
5.0%
6 208
 
4.7%
5 187
 
4.2%
4 415
 
9.4%
3 474
 
10.7%
2 438
 
9.9%
1 1558
35.3%
0 586
 
13.3%

Over18
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
True
4410 
ValueCountFrequency (%)
True 4410
100.0%
2024-01-25T19:58:48.241403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:48.312999image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6591075
Coefficient of variation (CV)0.24058002
Kurtosis-0.30263839
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82056898
Sum67074
Variance13.389068
MonotonicityNot monotonic
2024-01-25T19:58:48.393880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 630
14.3%
13 627
14.2%
14 603
13.7%
12 594
13.5%
15 303
6.9%
18 267
6.1%
17 246
 
5.6%
16 234
 
5.3%
19 228
 
5.2%
22 168
 
3.8%
Other values (5) 510
11.6%
ValueCountFrequency (%)
11 630
14.3%
12 594
13.5%
13 627
14.2%
14 603
13.7%
15 303
6.9%
16 234
 
5.3%
17 246
 
5.6%
18 267
6.1%
19 228
 
5.2%
20 165
 
3.7%
ValueCountFrequency (%)
25 54
 
1.2%
24 63
 
1.4%
23 84
 
1.9%
22 168
3.8%
21 144
3.3%
20 165
3.7%
19 228
5.2%
18 267
6.1%
17 246
5.6%
16 234
5.3%

StandardHours
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
8
4410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 4410
100.0%

Length

2024-01-25T19:58:48.490886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:48.584394image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
8 4410
100.0%

Most occurring characters

ValueCountFrequency (%)
8 4410
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 4410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 4410
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 4410
100.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1893 
1
1788 
2
474 
3
255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

Length

2024-01-25T19:58:48.661389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:48.764901image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1893
42.9%
1 1788
40.5%
2 474
 
10.7%
3 255
 
5.8%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.9%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean11.279936
Minimum0
Maximum40
Zeros33
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:48.872049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7822221
Coefficient of variation (CV)0.6899172
Kurtosis0.912936
Mean11.279936
Median Absolute Deviation (MAD)4
Skewness1.1168318
Sum49643
Variance60.562981
MonotonicityNot monotonic
2024-01-25T19:58:48.989071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 605
 
13.7%
6 375
 
8.5%
8 307
 
7.0%
9 287
 
6.5%
5 264
 
6.0%
7 243
 
5.5%
1 242
 
5.5%
4 189
 
4.3%
12 144
 
3.3%
3 126
 
2.9%
Other values (30) 1619
36.7%
ValueCountFrequency (%)
0 33
 
0.7%
1 242
5.5%
2 93
 
2.1%
3 126
 
2.9%
4 189
4.3%
5 264
6.0%
6 375
8.5%
7 243
5.5%
8 307
7.0%
9 287
6.5%
ValueCountFrequency (%)
40 6
 
0.1%
38 3
 
0.1%
37 12
0.3%
36 18
0.4%
35 9
 
0.2%
34 15
0.3%
33 21
0.5%
32 27
0.6%
31 27
0.6%
30 21
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros162
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:49.090580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2889782
Coefficient of variation (CV)0.46046122
Kurtosis0.491149
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55274763
Sum12345
Variance1.6614647
MonotonicityNot monotonic
2024-01-25T19:58:49.172582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1641
37.2%
3 1473
33.4%
4 369
 
8.4%
5 357
 
8.1%
1 213
 
4.8%
6 195
 
4.4%
0 162
 
3.7%
ValueCountFrequency (%)
0 162
 
3.7%
1 213
 
4.8%
2 1641
37.2%
3 1473
33.4%
4 369
 
8.4%
5 357
 
8.1%
6 195
 
4.4%
ValueCountFrequency (%)
6 195
 
4.4%
5 357
 
8.1%
4 369
 
8.4%
3 1473
33.4%
2 1641
37.2%
1 213
 
4.8%
0 162
 
3.7%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros132
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:49.282603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1251354
Coefficient of variation (CV)0.87400011
Kurtosis3.9238642
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7633282
Sum30906
Variance37.517284
MonotonicityNot monotonic
2024-01-25T19:58:49.401120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 588
13.3%
1 513
11.6%
3 384
8.7%
2 381
8.6%
10 360
8.2%
4 330
 
7.5%
7 270
 
6.1%
9 246
 
5.6%
8 240
 
5.4%
6 228
 
5.2%
Other values (27) 870
19.7%
ValueCountFrequency (%)
0 132
 
3.0%
1 513
11.6%
2 381
8.6%
3 384
8.7%
4 330
7.5%
5 588
13.3%
6 228
 
5.2%
7 270
6.1%
8 240
5.4%
9 246
5.6%
ValueCountFrequency (%)
40 3
 
0.1%
37 3
 
0.1%
36 6
 
0.1%
34 3
 
0.1%
33 15
0.3%
32 9
0.2%
31 9
0.2%
30 3
 
0.1%
29 6
 
0.1%
27 6
 
0.1%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros1743
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:49.512207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2216993
Coefficient of variation (CV)1.4726051
Kurtosis3.6017605
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.9829392
Sum9648
Variance10.379347
MonotonicityNot monotonic
2024-01-25T19:58:49.826730image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1743
39.5%
1 1071
24.3%
2 477
 
10.8%
7 228
 
5.2%
4 183
 
4.1%
3 156
 
3.5%
5 135
 
3.1%
6 96
 
2.2%
11 72
 
1.6%
8 54
 
1.2%
Other values (6) 195
 
4.4%
ValueCountFrequency (%)
0 1743
39.5%
1 1071
24.3%
2 477
 
10.8%
3 156
 
3.5%
4 183
 
4.1%
5 135
 
3.1%
6 96
 
2.2%
7 228
 
5.2%
8 54
 
1.2%
9 51
 
1.2%
ValueCountFrequency (%)
15 39
 
0.9%
14 27
 
0.6%
13 30
 
0.7%
12 30
 
0.7%
11 72
 
1.6%
10 18
 
0.4%
9 51
 
1.2%
8 54
 
1.2%
7 228
5.2%
6 96
2.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros789
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-01-25T19:58:49.933245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5673267
Coefficient of variation (CV)0.86519886
Kurtosis0.16794854
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83288361
Sum18183
Variance12.72582
MonotonicityNot monotonic
2024-01-25T19:58:50.024754image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 1032
23.4%
0 789
17.9%
7 648
14.7%
3 426
9.7%
8 321
 
7.3%
4 294
 
6.7%
1 228
 
5.2%
9 192
 
4.4%
5 93
 
2.1%
6 87
 
2.0%
Other values (8) 300
 
6.8%
ValueCountFrequency (%)
0 789
17.9%
1 228
 
5.2%
2 1032
23.4%
3 426
9.7%
4 294
 
6.7%
5 93
 
2.1%
6 87
 
2.0%
7 648
14.7%
8 321
 
7.3%
9 192
 
4.4%
ValueCountFrequency (%)
17 21
 
0.5%
16 6
 
0.1%
15 15
 
0.3%
14 15
 
0.3%
13 42
 
1.0%
12 54
 
1.2%
11 66
 
1.5%
10 81
 
1.8%
9 192
4.4%
8 321
7.3%
Distinct4
Distinct (%)0.1%
Missing25
Missing (%)0.6%
Memory size34.6 KiB
3.0
1350 
4.0
1334 
2.0
856 
1.0
845 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13155
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 1350
30.6%
4.0 1334
30.2%
2.0 856
19.4%
1.0 845
19.2%
(Missing) 25
 
0.6%

Length

2024-01-25T19:58:50.124261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:50.225769image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1350
30.8%
4.0 1334
30.4%
2.0 856
19.5%
1.0 845
19.3%

Most occurring characters

ValueCountFrequency (%)
. 4385
33.3%
0 4385
33.3%
3 1350
 
10.3%
4 1334
 
10.1%
2 856
 
6.5%
1 845
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8770
66.7%
Other Punctuation 4385
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4385
50.0%
3 1350
 
15.4%
4 1334
 
15.2%
2 856
 
9.8%
1 845
 
9.6%
Other Punctuation
ValueCountFrequency (%)
. 4385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4385
33.3%
0 4385
33.3%
3 1350
 
10.3%
4 1334
 
10.1%
2 856
 
6.5%
1 845
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4385
33.3%
0 4385
33.3%
3 1350
 
10.3%
4 1334
 
10.1%
2 856
 
6.5%
1 845
 
6.4%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.1%
Missing20
Missing (%)0.5%
Memory size34.6 KiB
4.0
1367 
3.0
1323 
1.0
860 
2.0
840 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13170
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
4.0 1367
31.0%
3.0 1323
30.0%
1.0 860
19.5%
2.0 840
19.0%
(Missing) 20
 
0.5%

Length

2024-01-25T19:58:50.319279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:50.422791image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1367
31.1%
3.0 1323
30.1%
1.0 860
19.6%
2.0 840
19.1%

Most occurring characters

ValueCountFrequency (%)
. 4390
33.3%
0 4390
33.3%
4 1367
 
10.4%
3 1323
 
10.0%
1 860
 
6.5%
2 840
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8780
66.7%
Other Punctuation 4390
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4390
50.0%
4 1367
 
15.6%
3 1323
 
15.1%
1 860
 
9.8%
2 840
 
9.6%
Other Punctuation
ValueCountFrequency (%)
. 4390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4390
33.3%
0 4390
33.3%
4 1367
 
10.4%
3 1323
 
10.0%
1 860
 
6.5%
2 840
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4390
33.3%
0 4390
33.3%
4 1367
 
10.4%
3 1323
 
10.0%
1 860
 
6.5%
2 840
 
6.4%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.1%
Missing38
Missing (%)0.9%
Memory size34.6 KiB
3.0
2660 
2.0
1019 
4.0
454 
1.0
 
239

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13116
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 2660
60.3%
2.0 1019
 
23.1%
4.0 454
 
10.3%
1.0 239
 
5.4%
(Missing) 38
 
0.9%

Length

2024-01-25T19:58:50.515500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:50.620008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 2660
60.8%
2.0 1019
 
23.3%
4.0 454
 
10.4%
1.0 239
 
5.5%

Most occurring characters

ValueCountFrequency (%)
. 4372
33.3%
0 4372
33.3%
3 2660
20.3%
2 1019
 
7.8%
4 454
 
3.5%
1 239
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8744
66.7%
Other Punctuation 4372
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4372
50.0%
3 2660
30.4%
2 1019
 
11.7%
4 454
 
5.2%
1 239
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 4372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4372
33.3%
0 4372
33.3%
3 2660
20.3%
2 1019
 
7.8%
4 454
 
3.5%
1 239
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4372
33.3%
0 4372
33.3%
3 2660
20.3%
2 1019
 
7.8%
4 454
 
3.5%
1 239
 
1.8%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
2604 
2
1125 
4
432 
1
 
249

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

Length

2024-01-25T19:58:50.712516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:50.814023image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2604
59.0%
2 1125
25.5%
4 432
 
9.8%
1 249
 
5.6%

PerformanceRating
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
3732 
4
678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4410
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Length

2024-01-25T19:58:50.903533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-25T19:58:50.998697image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4410
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3732
84.6%
4 678
 
15.4%

Interactions

2024-01-25T19:58:42.842144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:30.871073image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.051851image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.162724image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.347515image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.698992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.809917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.959393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.159310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.315810image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.667147image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.949174image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.037526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.146880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.260807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.447561image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.791016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.907108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.055360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.257296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.413895image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.764298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.052933image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.126665image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.236449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.376926image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.547466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.887859image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.002009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.155412image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.353257image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.684503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.861249image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.162156image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.225441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.335670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.484888image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.655911image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.988902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.105877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.263309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.455294image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.789510image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.966100image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.273144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.332650image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.442138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.599052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.764629image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.097747image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.212898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.378506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.568610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.901625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.077992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.372104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.422445image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.534571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.697054image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.868657image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.186777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.306835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.477960image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.665646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.002548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.175245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.479025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.522450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.634666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.803135image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.988860image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.288743image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.406865image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.585561image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.771592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.110696image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.281358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.592389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.628416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.742733image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.913185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.252461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.395938image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.518049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.702791image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.885406image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.222841image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.394425image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.699534image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.727436image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.841633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.017172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.359624image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.493803image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.621186image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.810758image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.986422image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.329764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.499381image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.815501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.834587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:32.949813image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.127245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.474979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.601048image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.735079image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:38.924946image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.096537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.441858image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.613745image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:43.926651image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:31.940548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:33.056061image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:34.239389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:35.585854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:36.704941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:37.850267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:39.037400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:40.203649image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:41.554063image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-01-25T19:58:42.724766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2024-01-25T19:58:51.105238image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeIDEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedPercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2220.0760.048-0.0050.0670.0600.0090.0660.0510.0560.0650.0780.0300.155-0.0260.353-0.0340.0560.0540.657-0.0440.0710.2520.1740.195
Attrition0.2221.0000.1260.078-0.0010.0190.097-0.0050.1190.0090.0340.0230.0620.1050.176-0.0240.0290.0310.0170.003-0.198-0.0390.103-0.190-0.053-0.175
BusinessTravel0.0760.1261.0000.0530.0140.0340.071-0.0020.0290.0370.0400.0550.0350.0330.046-0.0110.036-0.0280.0130.0260.025-0.0330.000-0.022-0.035-0.024
Department0.0480.0780.0531.0000.0230.0160.590-0.0010.0320.0000.0350.0370.0380.0120.047-0.040-0.020-0.0060.0250.034-0.0190.0170.0160.0080.0050.012
DistanceFromHome-0.005-0.0010.0140.0231.0000.0710.053-0.0020.0700.0450.0660.0450.0560.0610.081-0.025-0.0360.0390.0700.078-0.0000.0070.0660.0180.0090.034
Education0.0670.0190.0340.0160.0711.0000.051-0.0090.0250.0340.0490.0410.0650.0320.006-0.000-0.012-0.0310.0740.0360.0120.0310.0260.0040.0150.018
EducationField0.0600.0970.0710.5900.0530.0511.000-0.0020.0520.0270.0370.0290.0370.0310.0530.0040.007-0.0020.0170.0380.029-0.0220.0360.0260.0460.020
EmployeeID0.009-0.005-0.002-0.001-0.002-0.009-0.0021.0000.0000.0000.0000.0220.0000.0000.0000.006-0.000-0.0030.0270.0000.005-0.0100.0000.0050.0070.009
EnvironmentSatisfaction0.0660.1190.0290.0320.0700.0250.0520.0001.0000.0520.0200.0500.0380.0220.041-0.0050.007-0.0060.0300.030-0.0130.0170.0280.0080.026-0.003
Gender0.0510.0090.0370.0000.0450.0340.0270.0000.0521.0000.0090.0380.0340.0330.0320.012-0.072-0.0040.0490.011-0.045-0.0370.053-0.011-0.025-0.000
JobInvolvement0.0560.0340.0400.0350.0660.0490.0370.0000.0200.0091.0000.0400.0680.0320.0410.0170.022-0.0150.0000.002-0.002-0.0050.040-0.0060.0180.003
JobLevel0.0650.0230.0550.0370.0450.0410.0290.0220.0500.0380.0401.0000.0590.0290.0340.048-0.0100.0350.0330.049-0.031-0.0410.038-0.044-0.043-0.040
JobRole0.0780.0620.0350.0380.0560.0650.0370.0000.0380.0340.0680.0591.0000.0410.0650.002-0.015-0.0110.0580.072-0.0220.0690.062-0.027-0.0460.006
JobSatisfaction0.0300.1050.0330.0120.0610.0320.0310.0000.0220.0330.0320.0290.0411.0000.017-0.002-0.0520.0250.0560.047-0.012-0.0200.0270.0140.008-0.014
MaritalStatus0.1550.1760.0460.0470.0810.0060.0530.0000.0410.0320.0410.0340.0650.0171.000-0.063-0.0550.0050.0000.025-0.0930.0110.035-0.071-0.018-0.047
MonthlyIncome-0.026-0.024-0.011-0.040-0.025-0.0000.0040.006-0.0050.0120.0170.0480.002-0.002-0.0631.000-0.0420.0130.0100.059-0.0190.0160.0560.0290.0600.026
NumCompaniesWorked0.3530.0290.036-0.020-0.036-0.0120.007-0.0000.007-0.0720.022-0.010-0.015-0.052-0.055-0.0421.0000.0150.0650.0700.317-0.0130.083-0.170-0.067-0.143
PercentSalaryHike-0.0340.031-0.028-0.0060.039-0.031-0.002-0.003-0.006-0.004-0.0150.035-0.0110.0250.0050.0130.0151.0000.9990.067-0.036-0.0360.069-0.042-0.038-0.047
PerformanceRating0.0560.0170.0130.0250.0700.0740.0170.0270.0300.0490.0000.0330.0580.0560.0000.0100.0650.9991.0000.035-0.012-0.0270.051-0.001-0.014-0.001
StockOptionLevel0.0540.0030.0260.0340.0780.0360.0380.0000.0300.0110.0020.0490.0720.0470.0250.0590.0700.0670.0351.0000.014-0.0670.041-0.0010.0040.000
TotalWorkingYears0.657-0.1980.025-0.019-0.0000.0120.0290.005-0.013-0.045-0.002-0.031-0.022-0.012-0.093-0.0190.317-0.036-0.0120.0141.000-0.0410.0640.5940.3350.495
TrainingTimesLastYear-0.044-0.039-0.0330.0170.0070.031-0.022-0.0100.017-0.037-0.005-0.0410.069-0.0200.0110.016-0.013-0.036-0.027-0.067-0.0411.0000.050-0.0140.001-0.015
WorkLifeBalance0.0710.1030.0000.0160.0660.0260.0360.0000.0280.0530.0400.0380.0620.0270.0350.0560.0830.0690.0510.0410.0640.0501.0000.0080.003-0.000
YearsAtCompany0.252-0.190-0.0220.0080.0180.0040.0260.0050.008-0.011-0.006-0.044-0.0270.014-0.0710.029-0.170-0.042-0.001-0.0010.594-0.0140.0081.0000.5200.843
YearsSinceLastPromotion0.174-0.053-0.0350.0050.0090.0150.0460.0070.026-0.0250.018-0.043-0.0460.008-0.0180.060-0.067-0.038-0.0140.0040.3350.0010.0030.5201.0000.467
YearsWithCurrManager0.195-0.175-0.0240.0120.0340.0180.0200.009-0.003-0.0000.003-0.0400.006-0.014-0.0470.026-0.143-0.047-0.0010.0000.495-0.015-0.0000.8430.4671.000

Missing values

2024-01-25T19:58:44.125284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-25T19:58:44.522477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-25T19:58:44.769834image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EmployeeIDAgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountGenderJobLevelJobRoleMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18PercentSalaryHikeStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerEnvironmentSatisfactionJobSatisfactionWorkLifeBalanceJobInvolvementPerformanceRating
0151NoTravel_RarelySales62Life Sciences1Female1Healthcare RepresentativeMarried1311601.0Y11801.061003.04.02.033
1231YesTravel_FrequentlyResearch & Development101Life Sciences1Female1Research ScientistSingle418900.0Y23816.035143.02.04.024
2332NoTravel_FrequentlyResearch & Development174Other1Male4Sales ExecutiveMarried1932801.0Y15835.025032.02.01.033
3438NoNon-TravelResearch & Development25Life Sciences1Male3Human ResourcesMarried832103.0Y118313.058754.04.03.023
4532NoTravel_RarelyResearch & Development101Medical1Male1Sales ExecutiveSingle234204.0Y12829.026044.01.03.033
5646NoTravel_RarelyResearch & Development83Life Sciences1Female4Research DirectorMarried407103.0Y138028.057773.02.02.033
6728YesTravel_RarelyResearch & Development112Medical1Male2Sales ExecutiveSingle581302.0Y20815.020001.03.01.034
7829NoTravel_RarelyResearch & Development183Life Sciences1Male2Sales ExecutiveMarried314302.0Y228310.020001.02.03.034
8931NoTravel_RarelyResearch & Development13Life Sciences1Male3Laboratory TechnicianMarried204400.0Y218010.029782.04.03.034
91025NoNon-TravelResearch & Development74Medical1Female4Laboratory TechnicianDivorced1346401.0Y13816.026152.01.03.033
EmployeeIDAgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountGenderJobLevelJobRoleMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18PercentSalaryHikeStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerEnvironmentSatisfactionJobSatisfactionWorkLifeBalanceJobInvolvementPerformanceRating
4400440137NoTravel_RarelyResearch & Development225Medical1Female2Manufacturing DirectorMarried305502.0Y148317.033023.01.02.033
4401440245NoTravel_FrequentlySales211Marketing1Male3Research ScientistMarried228904.0Y13809.033021.03.03.023
4402440337YesTravel_FrequentlySales23Marketing1Male1Laboratory TechnicianDivorced400106.0Y118117.021001.03.03.033
4403440439NoTravel_FrequentlyResearch & Development223Medical1Female1Manufacturing DirectorSingle1296500.0Y198120.02191183.03.03.033
4404440529NoTravel_RarelySales43Other1Female2Human ResourcesSingle353901.0Y18806.026153.04.03.023
4405440642NoTravel_RarelyResearch & Development54Medical1Female1Research ScientistSingle602903.0Y178110.053024.01.03.033
4406440729NoTravel_RarelyResearch & Development24Medical1Male1Laboratory TechnicianDivorced267902.0Y158010.023024.04.03.023
4407440825NoTravel_RarelyResearch & Development252Life Sciences1Male2Sales ExecutiveMarried370200.0Y20805.044121.03.03.034
4408440942NoTravel_RarelySales182Medical1Male1Laboratory TechnicianDivorced239800.0Y148110.029784.01.03.023
4409441040NoTravel_RarelyResearch & Development283Medical1Male2Laboratory TechnicianDivorced546800.0Y1280NaN621391.03.0NaN43